import datetime import logging import multiprocessing as mp import os import queue import signal import subprocess as sp import threading import time import cv2 from setproctitle import setproctitle from frigate.camera import CameraMetrics, PTZMetrics from frigate.comms.config_updater import ConfigSubscriber from frigate.comms.inter_process import InterProcessRequestor from frigate.config import CameraConfig, DetectConfig, ModelConfig from frigate.const import ( CACHE_DIR, CACHE_SEGMENT_FORMAT, REQUEST_REGION_GRID, ) from frigate.log import LogPipe from frigate.motion import MotionDetector from frigate.motion.improved_motion import ImprovedMotionDetector from frigate.object_detection import RemoteObjectDetector from frigate.ptz.autotrack import ptz_moving_at_frame_time from frigate.track import ObjectTracker from frigate.track.norfair_tracker import NorfairTracker from frigate.track.object_attribute import ObjectAttribute from frigate.util.builtin import EventsPerSecond, get_tomorrow_at_time from frigate.util.image import ( FrameManager, SharedMemoryFrameManager, draw_box_with_label, ) from frigate.util.object import ( create_tensor_input, get_cluster_candidates, get_cluster_region, get_cluster_region_from_grid, get_min_region_size, get_startup_regions, inside_any, intersects_any, is_object_filtered, reduce_detections, ) from frigate.util.services import listen logger = logging.getLogger(__name__) def stop_ffmpeg(ffmpeg_process, logger): logger.info("Terminating the existing ffmpeg process...") ffmpeg_process.terminate() try: logger.info("Waiting for ffmpeg to exit gracefully...") ffmpeg_process.communicate(timeout=30) except sp.TimeoutExpired: logger.info("FFmpeg didn't exit. Force killing...") ffmpeg_process.kill() ffmpeg_process.communicate() ffmpeg_process = None def start_or_restart_ffmpeg( ffmpeg_cmd, logger, logpipe: LogPipe, frame_size=None, ffmpeg_process=None ): if ffmpeg_process is not None: stop_ffmpeg(ffmpeg_process, logger) if frame_size is None: process = sp.Popen( ffmpeg_cmd, stdout=sp.DEVNULL, stderr=logpipe, stdin=sp.DEVNULL, start_new_session=True, ) else: process = sp.Popen( ffmpeg_cmd, stdout=sp.PIPE, stderr=logpipe, stdin=sp.DEVNULL, bufsize=frame_size * 10, start_new_session=True, ) return process def capture_frames( ffmpeg_process, config: CameraConfig, shm_frame_count: int, frame_shape, frame_manager: FrameManager, frame_queue, fps: mp.Value, skipped_fps: mp.Value, current_frame: mp.Value, stop_event: mp.Event, ): frame_size = frame_shape[0] * frame_shape[1] frame_rate = EventsPerSecond() frame_rate.start() skipped_eps = EventsPerSecond() skipped_eps.start() shm_frames: list[str] = [] while True: fps.value = frame_rate.eps() skipped_fps.value = skipped_eps.eps() current_frame.value = datetime.datetime.now().timestamp() frame_name = f"{config.name}{current_frame.value}" frame_buffer = frame_manager.create(frame_name, frame_size) try: frame_buffer[:] = ffmpeg_process.stdout.read(frame_size) # update frame cache and cleanup existing frames shm_frames.append(frame_name) if len(shm_frames) > shm_frame_count: expired_frame_name = shm_frames.pop(0) frame_manager.delete(expired_frame_name) except Exception: # always delete the frame frame_manager.delete(frame_name) # shutdown has been initiated if stop_event.is_set(): break logger.error(f"{config.name}: Unable to read frames from ffmpeg process.") if ffmpeg_process.poll() is not None: logger.error( f"{config.name}: ffmpeg process is not running. exiting capture thread..." ) break continue frame_rate.update() # don't lock the queue to check, just try since it should rarely be full try: # add to the queue frame_queue.put(current_frame.value, False) frame_manager.close(frame_name) except queue.Full: # if the queue is full, skip this frame skipped_eps.update() # clear out frames for frame in shm_frames: frame_manager.delete(frame) class CameraWatchdog(threading.Thread): def __init__( self, camera_name, config: CameraConfig, shm_frame_count: int, frame_queue, camera_fps, skipped_fps, ffmpeg_pid, stop_event, ): threading.Thread.__init__(self) self.logger = logging.getLogger(f"watchdog.{camera_name}") self.camera_name = camera_name self.config = config self.shm_frame_count = shm_frame_count self.capture_thread = None self.ffmpeg_detect_process = None self.logpipe = LogPipe(f"ffmpeg.{self.camera_name}.detect") self.ffmpeg_other_processes: list[dict[str, any]] = [] self.camera_fps = camera_fps self.skipped_fps = skipped_fps self.ffmpeg_pid = ffmpeg_pid self.frame_queue = frame_queue self.frame_shape = self.config.frame_shape_yuv self.frame_size = self.frame_shape[0] * self.frame_shape[1] self.fps_overflow_count = 0 self.stop_event = stop_event self.sleeptime = self.config.ffmpeg.retry_interval def run(self): self.start_ffmpeg_detect() for c in self.config.ffmpeg_cmds: if "detect" in c["roles"]: continue logpipe = LogPipe( f"ffmpeg.{self.camera_name}.{'_'.join(sorted(c['roles']))}" ) self.ffmpeg_other_processes.append( { "cmd": c["cmd"], "roles": c["roles"], "logpipe": logpipe, "process": start_or_restart_ffmpeg(c["cmd"], self.logger, logpipe), } ) time.sleep(self.sleeptime) while not self.stop_event.wait(self.sleeptime): now = datetime.datetime.now().timestamp() if not self.capture_thread.is_alive(): self.camera_fps.value = 0 self.logger.error( f"Ffmpeg process crashed unexpectedly for {self.camera_name}." ) self.logger.error( "The following ffmpeg logs include the last 100 lines prior to exit." ) self.logpipe.dump() self.start_ffmpeg_detect() elif now - self.capture_thread.current_frame.value > 20: self.camera_fps.value = 0 self.logger.info( f"No frames received from {self.camera_name} in 20 seconds. Exiting ffmpeg..." ) self.ffmpeg_detect_process.terminate() try: self.logger.info("Waiting for ffmpeg to exit gracefully...") self.ffmpeg_detect_process.communicate(timeout=30) except sp.TimeoutExpired: self.logger.info("FFmpeg did not exit. Force killing...") self.ffmpeg_detect_process.kill() self.ffmpeg_detect_process.communicate() elif self.camera_fps.value >= (self.config.detect.fps + 10): self.fps_overflow_count += 1 if self.fps_overflow_count == 3: self.fps_overflow_count = 0 self.camera_fps.value = 0 self.logger.info( f"{self.camera_name} exceeded fps limit. Exiting ffmpeg..." ) self.ffmpeg_detect_process.terminate() try: self.logger.info("Waiting for ffmpeg to exit gracefully...") self.ffmpeg_detect_process.communicate(timeout=30) except sp.TimeoutExpired: self.logger.info("FFmpeg did not exit. Force killing...") self.ffmpeg_detect_process.kill() self.ffmpeg_detect_process.communicate() else: # process is running normally self.fps_overflow_count = 0 for p in self.ffmpeg_other_processes: poll = p["process"].poll() if self.config.record.enabled and "record" in p["roles"]: latest_segment_time = self.get_latest_segment_datetime( p.get( "latest_segment_time", datetime.datetime.now().astimezone(datetime.timezone.utc), ) ) if datetime.datetime.now().astimezone(datetime.timezone.utc) > ( latest_segment_time + datetime.timedelta(seconds=120) ): self.logger.error( f"No new recording segments were created for {self.camera_name} in the last 120s. restarting the ffmpeg record process..." ) p["process"] = start_or_restart_ffmpeg( p["cmd"], self.logger, p["logpipe"], ffmpeg_process=p["process"], ) continue else: p["latest_segment_time"] = latest_segment_time if poll is None: continue p["logpipe"].dump() p["process"] = start_or_restart_ffmpeg( p["cmd"], self.logger, p["logpipe"], ffmpeg_process=p["process"] ) stop_ffmpeg(self.ffmpeg_detect_process, self.logger) for p in self.ffmpeg_other_processes: stop_ffmpeg(p["process"], self.logger) p["logpipe"].close() self.logpipe.close() def start_ffmpeg_detect(self): ffmpeg_cmd = [ c["cmd"] for c in self.config.ffmpeg_cmds if "detect" in c["roles"] ][0] self.ffmpeg_detect_process = start_or_restart_ffmpeg( ffmpeg_cmd, self.logger, self.logpipe, self.frame_size ) self.ffmpeg_pid.value = self.ffmpeg_detect_process.pid self.capture_thread = CameraCapture( self.config, self.shm_frame_count, self.ffmpeg_detect_process, self.frame_shape, self.frame_queue, self.camera_fps, self.skipped_fps, self.stop_event, ) self.capture_thread.start() def get_latest_segment_datetime(self, latest_segment: datetime.datetime) -> int: """Checks if ffmpeg is still writing recording segments to cache.""" cache_files = sorted( [ d for d in os.listdir(CACHE_DIR) if os.path.isfile(os.path.join(CACHE_DIR, d)) and d.endswith(".mp4") and not d.startswith("preview_") ] ) newest_segment_time = latest_segment for file in cache_files: if self.camera_name in file: basename = os.path.splitext(file)[0] _, date = basename.rsplit("@", maxsplit=1) segment_time = datetime.datetime.strptime( date, CACHE_SEGMENT_FORMAT ).astimezone(datetime.timezone.utc) if segment_time > newest_segment_time: newest_segment_time = segment_time return newest_segment_time class CameraCapture(threading.Thread): def __init__( self, config: CameraConfig, shm_frame_count: int, ffmpeg_process, frame_shape, frame_queue, fps, skipped_fps, stop_event, ): threading.Thread.__init__(self) self.name = f"capture:{config.name}" self.config = config self.shm_frame_count = shm_frame_count self.frame_shape = frame_shape self.frame_queue = frame_queue self.fps = fps self.stop_event = stop_event self.skipped_fps = skipped_fps self.frame_manager = SharedMemoryFrameManager() self.ffmpeg_process = ffmpeg_process self.current_frame = mp.Value("d", 0.0) self.last_frame = 0 def run(self): capture_frames( self.ffmpeg_process, self.config, self.shm_frame_count, self.frame_shape, self.frame_manager, self.frame_queue, self.fps, self.skipped_fps, self.current_frame, self.stop_event, ) def capture_camera( name, config: CameraConfig, shm_frame_count: int, camera_metrics: CameraMetrics ): stop_event = mp.Event() def receiveSignal(signalNumber, frame): stop_event.set() signal.signal(signal.SIGTERM, receiveSignal) signal.signal(signal.SIGINT, receiveSignal) threading.current_thread().name = f"capture:{name}" setproctitle(f"frigate.capture:{name}") camera_watchdog = CameraWatchdog( name, config, shm_frame_count, camera_metrics.frame_queue, camera_metrics.camera_fps, camera_metrics.skipped_fps, camera_metrics.ffmpeg_pid, stop_event, ) camera_watchdog.start() camera_watchdog.join() def track_camera( name, config: CameraConfig, model_config, labelmap, detection_queue, result_connection, detected_objects_queue, camera_metrics: CameraMetrics, ptz_metrics: PTZMetrics, region_grid, ): stop_event = mp.Event() def receiveSignal(signalNumber, frame): stop_event.set() signal.signal(signal.SIGTERM, receiveSignal) signal.signal(signal.SIGINT, receiveSignal) threading.current_thread().name = f"process:{name}" setproctitle(f"frigate.process:{name}") listen() frame_queue = camera_metrics.frame_queue frame_shape = config.frame_shape objects_to_track = config.objects.track object_filters = config.objects.filters motion_detector = ImprovedMotionDetector( frame_shape, config.motion, config.detect.fps, name=config.name ) object_detector = RemoteObjectDetector( name, labelmap, detection_queue, result_connection, model_config, stop_event ) object_tracker = NorfairTracker(config, ptz_metrics) frame_manager = SharedMemoryFrameManager() # create communication for region grid updates requestor = InterProcessRequestor() process_frames( name, requestor, frame_queue, frame_shape, model_config, config.detect, frame_manager, motion_detector, object_detector, object_tracker, detected_objects_queue, camera_metrics, objects_to_track, object_filters, stop_event, ptz_metrics, region_grid, ) # empty the frame queue logger.info(f"{name}: emptying frame queue") while not frame_queue.empty(): frame_time = frame_queue.get(False) frame_manager.delete(f"{name}{frame_time}") logger.info(f"{name}: exiting subprocess") def detect( detect_config: DetectConfig, object_detector, frame, model_config, region, objects_to_track, object_filters, ): tensor_input = create_tensor_input(frame, model_config, region) detections = [] region_detections = object_detector.detect(tensor_input) for d in region_detections: box = d[2] size = region[2] - region[0] x_min = int(max(0, (box[1] * size) + region[0])) y_min = int(max(0, (box[0] * size) + region[1])) x_max = int(min(detect_config.width - 1, (box[3] * size) + region[0])) y_max = int(min(detect_config.height - 1, (box[2] * size) + region[1])) # ignore objects that were detected outside the frame if (x_min >= detect_config.width - 1) or (y_min >= detect_config.height - 1): continue width = x_max - x_min height = y_max - y_min area = width * height ratio = width / max(1, height) det = ( d[0], d[1], (x_min, y_min, x_max, y_max), area, ratio, region, ) # apply object filters if is_object_filtered(det, objects_to_track, object_filters): continue detections.append(det) return detections def process_frames( camera_name: str, requestor: InterProcessRequestor, frame_queue: mp.Queue, frame_shape, model_config: ModelConfig, detect_config: DetectConfig, frame_manager: FrameManager, motion_detector: MotionDetector, object_detector: RemoteObjectDetector, object_tracker: ObjectTracker, detected_objects_queue: mp.Queue, camera_metrics: CameraMetrics, objects_to_track: list[str], object_filters, stop_event, ptz_metrics: PTZMetrics, region_grid, exit_on_empty: bool = False, ): next_region_update = get_tomorrow_at_time(2) config_subscriber = ConfigSubscriber(f"config/detect/{camera_name}") fps_tracker = EventsPerSecond() fps_tracker.start() startup_scan = True stationary_frame_counter = 0 region_min_size = get_min_region_size(model_config) while not stop_event.is_set(): # check for updated detect config _, updated_detect_config = config_subscriber.check_for_update() if updated_detect_config: detect_config = updated_detect_config if ( datetime.datetime.now().astimezone(datetime.timezone.utc) > next_region_update ): region_grid = requestor.send_data(REQUEST_REGION_GRID, camera_name) next_region_update = get_tomorrow_at_time(2) try: if exit_on_empty: frame_time = frame_queue.get(False) else: frame_time = frame_queue.get(True, 1) except queue.Empty: if exit_on_empty: logger.info("Exiting track_objects...") break continue camera_metrics.detection_frame.value = frame_time ptz_metrics.frame_time.value = frame_time frame = frame_manager.get( f"{camera_name}{frame_time}", (frame_shape[0] * 3 // 2, frame_shape[1]) ) if frame is None: logger.debug(f"{camera_name}: frame {frame_time} is not in memory store.") continue # look for motion if enabled motion_boxes = motion_detector.detect(frame) regions = [] consolidated_detections = [] # if detection is disabled if not detect_config.enabled: object_tracker.match_and_update(frame_time, []) else: # get stationary object ids # check every Nth frame for stationary objects # disappeared objects are not stationary # also check for overlapping motion boxes if stationary_frame_counter == detect_config.stationary.interval: stationary_frame_counter = 0 stationary_object_ids = [] else: stationary_frame_counter += 1 stationary_object_ids = [ obj["id"] for obj in object_tracker.tracked_objects.values() # if it has exceeded the stationary threshold if obj["motionless_count"] >= detect_config.stationary.threshold # and it hasn't disappeared and object_tracker.disappeared[obj["id"]] == 0 # and it doesn't overlap with any current motion boxes when not calibrating and not intersects_any( obj["box"], [] if motion_detector.is_calibrating() else motion_boxes, ) ] # get tracked object boxes that aren't stationary tracked_object_boxes = [ ( # use existing object box for stationary objects obj["estimate"] if obj["motionless_count"] < detect_config.stationary.threshold else obj["box"] ) for obj in object_tracker.tracked_objects.values() if obj["id"] not in stationary_object_ids ] object_boxes = tracked_object_boxes + object_tracker.untracked_object_boxes # get consolidated regions for tracked objects regions = [ get_cluster_region( frame_shape, region_min_size, candidate, object_boxes ) for candidate in get_cluster_candidates( frame_shape, region_min_size, object_boxes ) ] # only add in the motion boxes when not calibrating and a ptz is not moving via autotracking # ptz_moving_at_frame_time() always returns False for non-autotracking cameras if not motion_detector.is_calibrating() and not ptz_moving_at_frame_time( frame_time, ptz_metrics.start_time.value, ptz_metrics.stop_time.value, ): # find motion boxes that are not inside tracked object regions standalone_motion_boxes = [ b for b in motion_boxes if not inside_any(b, regions) ] if standalone_motion_boxes: motion_clusters = get_cluster_candidates( frame_shape, region_min_size, standalone_motion_boxes, ) motion_regions = [ get_cluster_region_from_grid( frame_shape, region_min_size, candidate, standalone_motion_boxes, region_grid, ) for candidate in motion_clusters ] regions += motion_regions # if starting up, get the next startup scan region if startup_scan: for region in get_startup_regions( frame_shape, region_min_size, region_grid ): regions.append(region) startup_scan = False # resize regions and detect # seed with stationary objects detections = [ ( obj["label"], obj["score"], obj["box"], obj["area"], obj["ratio"], obj["region"], ) for obj in object_tracker.tracked_objects.values() if obj["id"] in stationary_object_ids ] for region in regions: detections.extend( detect( detect_config, object_detector, frame, model_config, region, objects_to_track, object_filters, ) ) consolidated_detections = reduce_detections(frame_shape, detections) # if detection was run on this frame, consolidate if len(regions) > 0: tracked_detections = [ d for d in consolidated_detections if d[0] not in model_config.all_attributes ] # now that we have refined our detections, we need to track objects object_tracker.match_and_update(frame_time, tracked_detections) # else, just update the frame times for the stationary objects else: object_tracker.update_frame_times(frame_time) # group the attribute detections based on what label they apply to attribute_detections: dict[str, ObjectAttribute] = {} for label, attribute_labels in model_config.attributes_map.items(): attribute_detections[label] = [ ObjectAttribute(d) for d in consolidated_detections if d[0] in attribute_labels ] # build detections detections = {} for obj in object_tracker.tracked_objects.values(): detections[obj["id"]] = {**obj, "attributes": []} # find the best object for each attribute to be assigned to all_objects: list[dict[str, any]] = object_tracker.tracked_objects.values() for attributes in attribute_detections.values(): for attribute in attributes: filtered_objects = filter( lambda o: o["label"] in attribute_detections.keys(), all_objects ) selected_object_id = attribute.find_best_object(filtered_objects) if selected_object_id is not None: detections[selected_object_id]["attributes"].append( attribute.get_tracking_data() ) # debug object tracking if False: bgr_frame = cv2.cvtColor( frame, cv2.COLOR_YUV2BGR_I420, ) object_tracker.debug_draw(bgr_frame, frame_time) cv2.imwrite( f"debug/frames/track-{'{:.6f}'.format(frame_time)}.jpg", bgr_frame ) # debug if False: bgr_frame = cv2.cvtColor( frame, cv2.COLOR_YUV2BGR_I420, ) for m_box in motion_boxes: cv2.rectangle( bgr_frame, (m_box[0], m_box[1]), (m_box[2], m_box[3]), (0, 0, 255), 2, ) for b in tracked_object_boxes: cv2.rectangle( bgr_frame, (b[0], b[1]), (b[2], b[3]), (255, 0, 0), 2, ) for obj in object_tracker.tracked_objects.values(): if obj["frame_time"] == frame_time: thickness = 2 color = model_config.colormap[obj["label"]] else: thickness = 1 color = (255, 0, 0) # draw the bounding boxes on the frame box = obj["box"] draw_box_with_label( bgr_frame, box[0], box[1], box[2], box[3], obj["label"], obj["id"], thickness=thickness, color=color, ) for region in regions: cv2.rectangle( bgr_frame, (region[0], region[1]), (region[2], region[3]), (0, 255, 0), 2, ) cv2.imwrite( f"debug/frames/{camera_name}-{'{:.6f}'.format(frame_time)}.jpg", bgr_frame, ) # add to the queue if not full if detected_objects_queue.full(): frame_manager.delete(f"{camera_name}{frame_time}") continue else: fps_tracker.update() camera_metrics.process_fps.value = fps_tracker.eps() detected_objects_queue.put( ( camera_name, frame_time, detections, motion_boxes, regions, ) ) camera_metrics.detection_fps.value = object_detector.fps.eps() frame_manager.close(f"{camera_name}{frame_time}") motion_detector.stop() requestor.stop() config_subscriber.stop()